Can we make Object Detection as human eyes+brain do?
Object detection can conceivably imitate what the human visual system does.  Research along these lines began in the 1980s in multiple laboratories and was termed Computer Vision.


  How do we see ... and detect things and their location so quick[?]  Is the reason that we have huge gigantic network in our brain and we are trained since birth to till now and still training[?]


Neurological visual systems continue to train as long as the eyes and neurons continue to function.  A person may, long after doing well in sports, learn how to catch glass objects that fall off a counter using peripheral vision, using the same neural pathways as were used for visually coordinated movement in sports but.  They are newly trained to the new cognitive intention of saving glassware and clean-up time.

The mapping of signals from the rods and cones to the visual cortex has been studied and some generalities have influenced artificial vision research and development.  The convolution kernels used in CNNs are partly derived from biological research.  In summary, the way mammals and other organisms with the equivalents of retinas see is via a set of transformations and pattern matching circuits.  The sequence of information types through biological visual networks can be summarized.


Light as a function of compound angle and time.
Edges in both space and time
Elements of objects and actions &mdash; These are real world features, not in the machine learning sense but in the natural language use of the term features.
Objects and actions


The development of object and action recognition in computers follows this same basic sequence.  The goal stated in the question is this.


  Achieve an AI to detect object in picture in human ways


Something like this has been the goal of many government, academic, and corporate laboratories, however the ability to recognize objects in still pictures is not as valuable as detecting the trajectories and other expressions of objects (such as changes in facial expressions) in real time.

Automated aiming, piloting, walking, and driving have been computer vision objectives since the first control systems were developed for anti-aircraft defense systems.  Considerable effort is being invested into how steering, breaking, and signaling can be automated in road vehicles.  A large array of household and industrial devices are being developed that rely on mapping the environment and navigating a robotic device through the map.

Stationary object recognition work is largely surrounding either categorizing images or drawing vector graphics from raster images, but these functions have limited application.  As mobile devices develop further and images on the web give way to videos on the web, a very strong and uninterrupted trend, the temporal dimension will continue to gain importance in the field computer vision.

None of these objectives are novel.  They are all based on animal capabilities, only some of which are uniquely human, and have been the subject of research since before the advent of digital computers.  However the feasibility of many designs has improved, largely due to a significant reduction in costs for fixed computing resources.  The vision system hardware of today costs roughly one thousandth of its 1990 cost.